P.092 Hirayama Disease: a diagnostic and therapeutic challenge
Bibliographic record
Abstract
Background: Hirayama disease (HD) is characterized by progressive cervical myelopathy caused by repetive neck flexion leading to forward displacement of the posterior dural sack with compression and injury of the spinal cord. Typically, the C7-T1 myotomes become weak and atrophic, while sparing sensation. Here we present two Canadian cases of this rare entity. Methods: Two cases of HD are presented and literature reviewed, showing the diagnostic and therapeutic challenges of this disease. Results: Case 1 is a 17-year-old male professional singer and musician. He presented with bilateral progressive hand weakness, which was aggrevated while playing the violine. Cervical MRI showed increased T2-weighted signaling at C5-7, but a correct diagnosis could not be identified. Eventually, dynamic cervical MRI showed the compression and he underwent an anterior cervical discectomy and fusion (ACDF) at C5-C6 and C6-C7 without complications. Case 2 is a 19-year-old female with progressive right hand weakness. After numerous investigations, a dynamic cervical MRI diagnosed her with HD with classic findings and she underwent an ACDF at C6-C7 without complications. Conclusions: Hiryama’s disease is rare, but should be kept in mind when cervical cord signal changes cannot be explained by standard MRI. Dynamic MRI is imperative to correct diagnosis and anterior fusion shows good outcomes in its management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".